Neurovista Holdings Inc., Cyberonics, Neurovista Corporation and BioNeuronics Corporation | Date: 2010-08-10
computer hardware; computer software for use with neurological monitors and/or neurological stimulators that are used to monitor and manage neurological and physiological disorders. implantable and noninvasive medical devices, namely, neurological monitors, monitoring leads and medical telemetry apparatus used with neurological monitors to diagnose, monitor and/or manage neurological and physiological disorders.
Neurovista Corporation | Date: 2012-02-13
Methods of classifying a subjects condition are described. The method includes: receiving measured signals from the subject; processing the measured signals using a computing device to identify a class associated with an identified condition of the subject; introducing an artificial class, the artificial class being associated with an unknown condition of the subject; classifying a feature vector from the subject into the identified class or the artificial class; and generating a signal in response to classifying the feature vector. The measured signals from the subject may include at least one signal extracted from brain activity of the subject.
NeuroVista Corporation | Date: 2010-10-14
Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.
Howbert J.J.,NeuroVista Inc |
Patterson E.E.,University of Minnesota |
Stead S.M.,Mayo Medical School |
Brinkmann B.,Mayo Medical School |
And 10 more authors.
PLoS ONE | Year: 2014
Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring. © 2014 Howbert et al. Source
Cook M.J.,University of Melbourne |
Varsavsky A.,University of Melbourne |
Himes D.,NeuroVista Inc |
Leyde K.,NeuroVista Inc |
And 3 more authors.
Frontiers in Neurology | Year: 2014
The pattern of epileptic seizures is often considered unpredictable, and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and manmade systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events. We postulated that human inter-seizure intervals would follow a power law relationship, and furthermore that evidence for the existence of a long memory process could be established in this relationship. We performed a post-hoc analysis, studying 8 patients who had long-term (up to 2 years) ambulatory intracranial EEG data recorded as part of the assessment of a novel seizure prediction device. We demonstrated that a power law relationship could be established in these patients (β =-1.5). In 5 out of the 6 subjects whose data was sufficiently stationary for analysis, we found evidence of long memory between epileptic events. This memory spans time scales from 30 minutes to 40 days. The estimated Hurst exponents range from 0.51-0.77±0.01. This finding may provide evidence of phasetransitions underlying the dynamics of epilepsy. © 2014 Cook, Varsavsky, Himes, Leyde, Berkovic, O'brien and Mareels. Source